What is Data Blending in Tableau?

Combining data from different spreadsheets or databases is a core task in any analysis, but it can be tricky when your sources don't neatly align. Tableau offers a powerful feature called data blending to solve this exact problem. This guide will walk you through what data blending is, how it differs from other methods like joins, and exactly when and how to use it.

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What is Data Blending in Tableau?

Data blending is a method used in Tableau to combine data from multiple, separate data sources on a single worksheet. Think of it as a way to create a view that uses a primary data source and then pulls in supplementary, aggregated information from a secondary data source.

The key thing to understand is that blending happens on a sheet-by-sheet basis, after the data has been aggregated. This is fundamentally different from a data join or using relationships, which combine raw, row-level data at the source before you even start building your viz.

Blending vs. Joins: An Analogy

To really get the difference, let's use a simple analogy:

• Joining Data is like taking two different spreadsheets (say, Sales and Customer Details) and stapling them together on a common column (like CustomerID) to create one big, wide master spreadsheet before you do any analysis. You are physically combining the raw data into one table.
• Blending Data is like having two separate spreadsheets on your desk. You create a chart using only the Sales data first. Then, you look at your chart (e.g., sales by city) and ask a helper to go look at the Customer Details spreadsheet and bring back just the summarized information you need for those specific cities, like the average customer age per city. The spreadsheets remain separate, you're just borrowing aggregated data from one to add context to the other.

In this analogy, the Sales data is your primary source, and the Customer Details data is your secondary source.

How Data Blending Works in Tableau

Once you grasp the concept, the mechanics of blending are straightforward. It’s a two-step process controlled by something called “linking fields.”

1. Establishing a Primary and Secondary Source

Whichever data source you use first on a worksheet automatically becomes the primary data source. You'll see its icon has a small blue checkmark. All subsequent data sources you bring into that same sheet become secondary data sources, marked with an orange checkmark.

This is crucial because the primary source determines the level of aggregation for your visualization. All data from the primary source is included, and only matching data from the secondary source is brought in.

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2. Active Linking Fields

For Tableau to know how to combine the data, you need at least one active "linking field" between the primary and secondary sources. An active link is a common dimension between your data sources that connects the two.

• Tableau automatically tries to create these links by identifying fields with the same name.
• When a field is a potential link, a small broken chain link icon appears next to it.
• When you use that field from the primary source and then pull a measure from the secondary source, the link becomes active, and the icon turns into a solid orange chain link.

If your common fields have different names (e.g., Region in one source and Sales_Region in another), you can manually define the relationship by going to Data &gt, Edit Blend Relationships.

This process acts like a kind of pseudo-left join on your aggregated data. It takes all the aggregated result rows from your primary source and looks up matching values in the aggregated secondary source based on the linking field(s).

When Should You Use Data Blending?

While Tableau's newer "relationship" model (the noodles) is now the default and preferred method for most cases, data blending still has specific circumstances where it is the best, or only, option.

When Your Data is at Different Levels of Granularity

This is the classic, number-one reason to use data blending. Imagine you have a database of daily sales transactions, but your sales targets are stored in a spreadsheet at a monthly level.

• If you were to join these two tables on Month, the monthly sales target of $10,000 would be duplicated for every single day in that month. Summing up your Target column after the join would give you a wildly inflated number ($10,000 * 30 days = $300,000).
• With blending, you can use the daily transaction data as your primary source to build a view of total sales by month. Then, you can bring in the Target field from your secondary (monthly) data source. Because blending aggregates the data before combining, it will bring in the correct, non-duplicated target value of $10,000 for each month.

When Combining Data from Different Database Types

Sometimes your data lives in entirely different systems. For instance, your website user data is in Google Analytics, but your sales data is in a Salesforce database or a local SQL server. You can’t perform a direct cross-database join between them. Blending allows you to connect to both sources independently and then combine their aggregated data within a single Tableau worksheet.

When Using Published Tableau Data Sources

If your team uses standardized, published data sources on a Tableau Server, you likely don't have permission to edit them. If you need to supplement that official data with a personal spreadsheet (like your own sales goals or region mapping), blending is the perfect way to do it without altering the original, sanctioned source.

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Step-by-Step Guide to Data Blending in Tableau

Let's walk through a simple, practical example. We have two data sources:

1. Order Data: An Excel sheet containing individual order details, including State and Sales.
2. Population Data: Another Excel sheet with population figures for each State.

Our goal is to create a simple table showing total sales and population for each state.

Step 1: Connect to Your Data Sources

First, connect to both of your data sources in Tableau. In the Data Source pane, click "Add" to connect to your first source (Order Data). Then, click "Add" again to connect to your second source (Population Data). You should now see both sources listed in the left-hand pane.

Step 2: Establish the Primary Data Source

Navigate to a new worksheet. In the Data pane, make sure your Order Data source is selected. Drag the State dimension to the Rows shelf and the Sales measure to the Text tile on the Marks card.

Because this was the first data source you used on the sheet, "Order Data" is now your primary source. You'll see a blue checkmark next to its name.

Step 3: Define the Linking Field

Now, click on the Population Data source in the Data pane. You should see a small, gray broken chain link icon next to the State field. This indicates Tableau recognizes it as a potential linking field because it shares the same name as a field in your primary source.

The link is not yet active. You need to use a field from this secondary source to activate it.

Step 4: Bring in Data from the Secondary Source

With the Population Data source still selected, drag the Population measure onto your view, perhaps placing it next to the SUM(Sales) column in the table. As soon as you drop it onto the canvas, two things happen:

• An orange checkmark appears next to the Population Data source, confirming it as the secondary source for this sheet.
• The link icon next to the State field becomes a solid orange chain link, showing it’s now an active link.

Your view now successfully shows the aggregated Sales from your primary source and the aggregated Population from your secondary source, linked together by State.

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Troubleshooting: Dealing with Asterisks (*)

A common issue when blending is seeing an asterisk (*) instead of a value from your secondary source. This happens when a single link from the primary source (like a single state) matches with multiple values in the secondary source. Because a blend only displays a single, aggregated value, Tableau displays an asterisk to let you know it doesn't know which of the multiple distinct values to show.

This is usually a sign that your linking fields aren't specific enough. Check your blend relationships and ensure you are linking on the field that provides a one-to-one or many-to-one relationship from the primary to the secondary source.

Data Blending vs. Relationships vs. Joins: A Quick Cheatsheet

Here’s a simple breakdown to help you decide which method to use.

Joins

• What it is: Combines row-level data from tables within the same data source.
• When it happens: In the Data Source pane, before analysis.
• Result: Creates a single, denormalized new table for Tableau to query.
• Best for: Combining tables from the same database where data has a consistent level of detail.

Relationships (Noodles)

• What it is: A flexible, modern way to define connections between logical tables from one or many databases.
• When it happens: In the Data Source pane, before analysis.
• Result: Tables remain separate and Tableau intelligently queries them at their native level of detail during analysis, preventing data duplication. This is now the recommended default.
• Best for: The majority of multi-table analysis cases, especially when tables have different levels of granularity.

Data Blending

• What it is: Combines aggregated data at the worksheet level from different data sources.
• When it happens: On an individual worksheet, after aggregation.
• Result: Primary and secondary sources are queried independently, aggregated, and the results are combined in the final visualization. It behaves like a left join on aggregated data.
• Best for: Cases with mismatched granularity, connecting cross-database sources, or supplementing published server data sources.

Final Thoughts

Data blending is a powerful technique in your Tableau toolkit, especially for handling data at different granularities or from disparate systems. By understanding how primary and secondary sources interact through linking fields, you can enrich your visualizations with context that would be difficult to achieve otherwise.

While Tableau gives you incredible control, setting it all up - joining tables, managing blend relationships, and even simply connecting to a dozen different marketing and sales platforms - is still time-consuming. We wanted to skip that entire setup process, which is why we created Graphed. We let you connect your data sources with a few clicks, then use simple, natural language to create the charts and reports you need. Instead of wrestling with data models, you can just ask, "Show me a chart of sales vs population by state," and get your answer instantly.